As society moves into the digital age, there are and will be problems of accessing similar data from different database. This can be related to searching for metadata about entertainment content, such as images, audio and video, or related to searching biometric data. For example, each content owner, service provider, or content aggregator of multimedia content or each organization that manages biometric and other identification data can and will have a different database, having a different database protocol (e.g., Oracle Oracle9i versus Microsoft SQL 2000), different database structures (e.g., database field arrangement), different classification schemes (e.g., names for fields and related entries), and different data formats (e.g., biometric data representations for biometric data sets such as facial images, fingerprints, voice signatures, handwritten signatures, iris or retinal images, etc.).
Different databases will exist because there are and will continue to be political, business and security issues in using a standard central database shared across organizations. For example, companies may feel that their database is superior, and thus, build and maintain their own databases for similar types of content that may overlap content represented differently in databases managed by others. There may be so much legacy content that a company, industry, standards body or government will not adopt a standard. In addition, a standard may produce security or privacy issues, such as central databases that know everything about a person or contain information about content across different companies. It is harder to secure certain fields than others on a central database since users have direct access. For example, certain aspects about a person, such as criminal history, may be accessible to only a police officer or FBI agent and not the general public. Similarly, the retail price for entertainment content may be accessible to only registered retailers, and not end users (i.e. consumers).
There are many reasons to search similar data across different databases. For example, identity theft is currently a critical issue. If similar data used to identify a particular individual such as facial images for face recognition, fingerprints, retinal scans, etc., can be searched across different databases structures (e.g., different databases), including using different biometric data representations and templates, the system can catch people that have or are obtaining multiple ID cards, and, thus, reduce identity fraud. Regarding entertainment content, selling metadata to the consumer is a method to increase revenues for an industry trying to deal with rampant digital piracy, as well as a method to fight piracy by providing advantages to legitimate services and purchased content. More specifically, a consumer may want to find songs from different music labels that fit into a specific genre and time period. In this case, there is a need for a method of searching for similar content that corresponds to the consumer's criteria (e.g., some form of song identifier) across different content owner databases to find the song metadata.
One novel method is to have a search-only TransMetaWarp router that knows how to convert (e.g., transform and/or warp—as defined in the detailed section below) search criteria between different databases with similar data. The databases can be different or of the same protocol (e.g., both are Oracle Oracle9i or one is Oracle Oracle9i and the other is Microsoft Access), database structures (e.g., fields arrangement), classification schemes (e.g., names for fields and related entries), and data formats (e.g., biometric data representations or templates and/or content compression methods such as MPEG, Windows Media Format, JPEG, or TIF).
In one embodiment, TransMetaWarp router can be applied to biometric databases, where, for example, different face recognition templates can be the search criteria, and compared via a TransMetaWarp router. This router knows how to convert the original face image to one or more templates, or, alternatively, compare one or more templates directly if either template can re-create the face image. For driver's licenses (DL), the TransMetaWarp router can be controlled by the federal government or some trusted entity, such that states can compare DL face images without having to talk directly or standardize with other states.
In addition, TransMetaWarp can be applied to entertainment content, where the search criteria are content types as described through metadata. For example, one music label may classify music as fast and happy, whereas a different music label may classify music as tempo=5 and mood=5. The TransMetaWarp router knows how to mathematically transform or has been programmed and/or trained how to warp these two (or more—such as when other record labels are added) databases with different classification schemes so that a user can search for music that fits into certain categories across record labels. The schemes can be converted to be compared directly to each other, or, alternatively, a universal search criteria, such as beat=rock and temperament=positive, can be used to inter-relate and search both database classification schemes.
An alternative embodiment is to have a master TransMetaWarp database not just transform the search, but additionally transform the data so it can be saved in a master format on a master database, creating a de-facto standard or using an existing standard. This alternative embodiment can be more efficient on the network when security and privacy issues can be controlled for a central system.
Finally, the flow of content and information during content distribution and consumption causes different participants to require different metadata. This creates further requirements for TransMetaWarp routers or databases. The architecture shows that metadata will have to be searched across various database types. In addition for this architecture, the content can be identified using a content identifier (ID), and this content ID can be provided as the search criteria to a TransMetaWarp router, which in response, searches and returns metadata from different participating databases.
One aspect of the invention is a method for searching for metadata relating to media content. The method receives a content signal, and transforms it into different hash formats, each corresponding to different databases storing hashes of content signals and corresponding identifying information. The identifying information is used to determine identifying information from the corresponding databases, and based on the identifying information, metadata is retrieved for the content signal. Several alternative implementations are described, including system embodiments, including network routing systems that search across several different network databases.